Calls for papers
International Journal of Applied Management Science
Special Issue on: "Modern Tools of Industrial Engineering: Applications in Decision Sciences"
Prof. Cengiz Kahraman, Istanbul Technical University, Turkey
Prof. Da Ruan, Belgian Nuclear Research Centre and Ghent University, Belgium
Modern industrial engineering tools include soft computing techniques such as fuzzy sets, neural networks, tabu search and ant colony techniques. Soft computing and computational intelligent techniques (SCCITs) are used to obtain the closest solutions to computationally-hard problems. SCCITs are vitally practical tools for many complex problems since they can be tolerant of imprecision, uncertainty, partial truth, and approximation. Traditional hard computing methods are often too cumbersome for complex problems. They need a precisely stated analytical model and often a lot of computational time (Zadeh, 1965).
Artificial neural networks, genetic algorithms, fuzzy logic models, ant colony techniques, tabu search etc. are the best known soft computing techniques in the literature. The technique of artificial neural networks (ANNs) is a computational model and is inspired by biological neural networks. Genetic algorithms (GAs) are global search and optimization techniques motivated by the process of natural selection in biological system (Gen and Cheng, 2000; Kaya, 2009). Ant colony optimization (ACO) is a cooperative search algorithm inspired by the behaviour of ants in finding paths from the nest to food. ACO is used for solving combinatorial optimization problems (Yang and Zhuang, 2010). Tabu search (TS) is another algorithm which is used for the solution of combinatorial optimization problems like the traveling salesman problem. TS method is based on the neighbourhood search procedure such that the algorithm iteratively moves from a solution to another solution in the related neighborhood, until it reaches any stopping criterion.
MCDM techniques are also known as SCCITs. MCDM techniques are used classified into multiple objective decision making (MODM) and MULTIPLE ATTRIBUTE DECISION MAKING (MADM). The difference between MADM and MODM is that MADM is associated with problems of which numbers of alternatives have been predetermined. The decision maker (DM) selects/ranks a finite number of courses of action. On the other hand, MODM is not associated with the problems in which alternatives have been predetermined. In other words, MODM techniques present optimization of an alternative or alternatives on the basis of prioritized objectives while MADM techniques present selection of an alternative from a set of alternatives based on prioritized attributes of the alternatives.
In this special issue, decision-making with modern industrial engineering tools will be published to include each tool with its application through a real case study. Thus, the papers contained in this special issue will be a representation of the latest decision-making techniques in industrial engineering.
We welcome submissions of practical research results in the field of decision sciences.Subject Coverage
Suitable topics include but are not limited to:
- Fuzzy sets
- Computational intelligent systems
- Multiple criteria decision making (MCDM)
- Soft computing
- Decision making with axioms
Notes for Prospective Authors
Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. (N.B. Conference papers may only be submitted if the paper was not originally copyrighted and if it has been completely re-written).
All papers are refereed through a peer review process. A guide for authors, sample copies and other relevant information for submitting papers are available on the Author Guidelines page
Submission due date of full paper: 5 March, 2011
Feedback from referees: 15 May, 2011
Submission due date of revised paper: 30 June, 2011
Notification of acceptance: 1 September, 2011
Submission of final revised paper: 1 October, 2011